Many (mostly elderly) people accidently fall, when staying alone in their house/flat/room, and can't recover on their own. This can leave them helpless for hours, sometimes even days. It can have severe consequences, and can even result in the death of the person
One has tried for a very long time to find a solution, where a system would automatically detect such a fall, and would activate a warning.
The most used system today is a device with a help button: when the person falls, and can't get up on its own, he/she can activate a button that will warn family/an emergency company or other. However, it has been shown that in real cases, in 4 out of 5 incidents, the person does/can't push this button (or unable to do so, or did not carry the device).
State of the art today is a system that one carries with them, and that contains some inertial sensors (acceleration and/or gyroscopes). This system will detect automatically a fall. If the person can't indicate after the fall that he/she is OK, it will again release a help request.
The big advantage is that it does not require an action of the person to activate the help call, but the person must wear the device. Also here, experiences shows that this is often not the case (one forgets to wear it when coming out of bed, after taking a shower, or one intentionally does not want to wear it since one does not want to be seen as aid-needed).
Also, since this system only detects the movement, and not the end position (is the person on the floor, or on a chair, . . . ) it often triggers falls alarms.
New investigations are now done on contactless systems, mostly based on normal visual cameras. Although that his solves the problem of the requirement to carry a device, these systems are not yet reliable enough. Another disadvantage is that a visual camera is perceived as a privacy intrusion. Trials are now done with dual visual cameras, so that one can calculate the distance to the person. But this is complicated and expensive.
Various groups are now working on using a Time of Flight camera, in order to have reliable distance data to distinguish objects. Even though it already gives more reliable results, the nr of falls alarms is still too high.
Another approach tried out is the use of PIR (pyro electrical passive infrared sensors). These are the type of sensors are also used for burglar alarms. Initially one used single devices, but nowadays there are also array sensors on the market. They detect movements of a warm object. But since these are AC sensors, they can't detect a warm object if it is standing still. Therefor one also has tried to combine this type of sensors with pressure sensors, in order to have more information about the position of the object.
Other attempts to use temperature as detection mean have used an temperature it sensor, to evaluate the situation, triggered by a pressure sensor, after the fall (design for fall detection system with floor pressure and infrared image). But the fall detection by pressure sensors is very limited. Also evaluating the final situation only by temperature is prone to errors. Another approach uses a temperature array to detect a fall from a toilet. Since it only relies on temperature, it is also very prone to the errors, since a very small temperature window is used to detect the position.
Till today, camera systems are not mature enough and therefore there are no fixed mounted systems on the fall detection market.
In the art sensor fusion, being the combining of sensory data or data derived from sensory data from disparate sources such that the resulting information is in some sense better than would be possible when these sources were used individually, wherein the term better in this case can mean more accurate, more complete, or more dependable, or refer to the result of an emerging view, is known but still does not provide sufficient performance.
Bringing data set of a different resolution to a same resolution by up- or down sampling one of the sets is known but does not alter performance.
Use in detection algorithms of multiple data sets of different nature in general is known (Julien Ros et al—A generative model for 3D sensors in the Bayesian Occupancy filter framework: Application for fusion in smart home monitoring, INFORMATION FUSION, 2012, 13th Conference), in particular the use of a first detection algorithm on a first set and use the result thereof as input for a second algorithm operable on a second set, but such technique still does not provide sufficient performance, nor do they improve the data sets quality itself.